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Abstract:
Graph mining methods enumerate frequent subgraphs
efficiently, but they are not necessarily good features for
machine learning due to high correlation among features.
Thus it makes sense to perform principal component analysis
to reduce the dimensionality and create decorrelated
features. We present a novel iterative mining algorithm
that captures informative patterns corresponding to major
entries of top principal components. It repeatedly calls
weighted substructure mining where example weights are
updated in each iteration. The Lanczos algorithm, a standard
algorithm of eigendecomposition, is employed to update
the weights. In experiments, our patterns are shown to
approximate the principal components obtained by frequent
mining.
